public OpenNlpDoccatRecommender(Recommender aRecommender, OpenNlpDoccatRecommenderTraits aTraits) { layerName = aRecommender.getLayer().getName(); featureName = aRecommender.getFeature().getName(); maxRecommendations = aRecommender.getMaxRecommendations(); traits = aTraits; }
public OpenNlpNerRecommender(Recommender aRecommender, OpenNlpNerRecommenderTraits aTraits) { layerName = aRecommender.getLayer().getName(); featureName = aRecommender.getFeature().getName(); maxRecommendations = aRecommender.getMaxRecommendations(); traits = aTraits; }
public StringMatchingRecommender(Recommender aRecommender, StringMatchingRecommenderTraits aTraits) { layerName = aRecommender.getLayer().getName(); featureName = aRecommender.getFeature().getName(); maxRecommendations = aRecommender.getMaxRecommendations(); traits = aTraits; }
public StringMatchingRecommender(Recommender aRecommender, StringMatchingRecommenderTraits aTraits) { layerName = aRecommender.getLayer().getName(); featureName = aRecommender.getFeature().getName(); maxRecommendations = aRecommender.getMaxRecommendations(); traits = aTraits; }
public OpenNlpPosRecommender(Recommender aRecommender, OpenNlpPosRecommenderTraits aTraits) { layerName = aRecommender.getLayer().getName(); featureName = aRecommender.getFeature().getName(); maxRecommendations = aRecommender.getMaxRecommendations(); traits = aTraits; }
private void predictToken(String aCoveredText, int aBegin, int aEnd, JCas aJcas) { List<KBHandle> handles = new ArrayList<>(); AnnotationFeature feat = recommender.getFeature(); FeatureSupport<ConceptFeatureTraits> fs = fsRegistry.getFeatureSupport(feat); ConceptFeatureTraits conceptFeatureTraits = fs.readTraits(feat); if (conceptFeatureTraits.getRepositoryId() != null) { Optional<KnowledgeBase> kb = kbService.getKnowledgeBaseById(recommender.getProject(), conceptFeatureTraits.getRepositoryId()); if (kb.isPresent() && kb.get().isSupportConceptLinking()) { handles.addAll(readCandidates(kb.get(), aCoveredText, aBegin, aJcas)); } } else { for (KnowledgeBase kb : kbService.getEnabledKnowledgeBases(recommender.getProject())) { if (kb.isSupportConceptLinking()) { handles.addAll(readCandidates(kb, aCoveredText, aBegin, aJcas)); } } } Type predictionType = getAnnotationType(aJcas.getCas(), PredictedSpan.class); Feature labelFeature = predictionType.getFeatureByBaseName("label"); for (KBHandle prediction : handles.stream().limit(recommender.getMaxRecommendations()) .collect(Collectors.toList())) { AnnotationFS annotation = aJcas.getCas().createAnnotation(predictionType, aBegin, aEnd); annotation.setStringValue(labelFeature, prediction.getIdentifier()); aJcas.getCas().addFsToIndexes(annotation); } }
private void predictToken(String aCoveredText, int aBegin, int aEnd, JCas aJcas) { List<KBHandle> handles = new ArrayList<>(); AnnotationFeature feat = recommender.getFeature(); FeatureSupport<ConceptFeatureTraits> fs = fsRegistry.getFeatureSupport(feat); ConceptFeatureTraits conceptFeatureTraits = fs.readTraits(feat); if (conceptFeatureTraits.getRepositoryId() != null) { Optional<KnowledgeBase> kb = kbService.getKnowledgeBaseById(recommender.getProject(), conceptFeatureTraits.getRepositoryId()); if (kb.isPresent() && kb.get().isSupportConceptLinking()) { handles.addAll(readCandidates(kb.get(), aCoveredText, aBegin, aJcas)); } } else { for (KnowledgeBase kb : kbService.getEnabledKnowledgeBases(recommender.getProject())) { if (kb.isSupportConceptLinking()) { handles.addAll(readCandidates(kb, aCoveredText, aBegin, aJcas)); } } } Type predictionType = getAnnotationType(aJcas.getCas(), PredictedSpan.class); Feature labelFeature = predictionType.getFeatureByBaseName("label"); for (KBHandle prediction : handles.stream().limit(recommender.getMaxRecommendations()) .collect(Collectors.toList())) { AnnotationFS annotation = aJcas.getCas().createAnnotation(predictionType, aBegin, aEnd); annotation.setStringValue(labelFeature, prediction.getIdentifier()); aJcas.getCas().addFsToIndexes(annotation); } }
@Override public void exportData(ProjectExportRequest aRequest, ExportedProject aExProject, File aFile) { Project project = aRequest.getProject(); List<ExportedRecommender> exportedRecommenders = new ArrayList<>(); for (Recommender recommender : recommendationService.listRecommenders(project)) { ExportedRecommender exportedRecommender = new ExportedRecommender(); exportedRecommender.setAlwaysSelected(recommender.isAlwaysSelected()); exportedRecommender.setFeature(recommender.getFeature().getName()); exportedRecommender.setEnabled(recommender.isEnabled()); exportedRecommender.setLayerName(recommender.getLayer().getName()); exportedRecommender.setName(recommender.getName()); exportedRecommender.setThreshold(recommender.getThreshold()); exportedRecommender.setTool(recommender.getTool()); exportedRecommender.setSkipEvaluation(recommender.isSkipEvaluation()); exportedRecommender.setMaxRecommendations(recommender.getMaxRecommendations()); exportedRecommender.setStatesIgnoredForTraining( recommender.getStatesIgnoredForTraining()); exportedRecommender.setTraits(recommender.getTraits()); exportedRecommenders.add(exportedRecommender); } aExProject.setProperty(KEY, exportedRecommenders); int n = exportedRecommenders.size(); LOG.info("Exported [{}] recommenders for project [{}]", n, project.getName()); }
if (recommender.getMaxRecommendations() < 1) { if (recommender.getMaxRecommendations() > MAX_RECOMMENDATIONS_CAP) {